Friday 5 February
Boyen & Koller (1998), Tractable
inference for Complex Stochastic Processes
Proceedings of the Fourteenth Annual Conference on Uncertainty in AI,
Introduced by Amos Storkey
Friday 19 February
Ghahramani, Z. and Jordan, M.I. (1997) Factorial
Hidden Markov Models , Machine Learning 29, 245-273.
Introduced by Will Lowe
Friday 5 March (NOTE MEETING TIME IS 1.00pm THIS WEEK)
J.F.G. de Freitas, M. Niranjan, A.H. Gee and A. Doucet (1998)
Monte Carlo Methods for Optimisation of Neural Network Models. Technical
report CUED/F-INFENG/TR 328 ,
Cambridge University, Department of Engineering, July 1998.
Introduced by Matthias Seeger.
Friday 19 March
M. Ostendorf and V. Digalakis and O. Kimball (1996)
HMMs to Segment Models: A Unified View of Stochastic Modeling for Speech
IEEE Trans. on Speech and Audio Processing 4, 360-378
Introduced by Simon King
Friday 2 April - This is Good Friday. No meeting this week.
Friday 16 April: Postponed to Friday 23 April
Friday 23 April
Learning multi-class dynamics,
Andrew Blake, Ben North and Michael Isard.
Advances in Neural Information Processing Systems 11, in press, MIT Press,
Introduced by Stephen Isard
Friday 7 May
An introduction to the junction tree algorithm: discussion.
Friday 21 May
David MacKay (1998) Introduction
to Monte Carlo Methods
(appears in Learning in Graphical Models, ed. M. I. Jordan, Kluwer 1998)
Introduced by Chris Williams
Friday 11 June
F. C. N. Pereira (1999) Speech
Recognition by Composition of Weighted Finite Automata.
Introduced by Paul Taylor
Friday 15 October
Geoffrey Hinton (1999) Products of
Introduced by Paul Taylor
Friday 29 October
A presentation by William Chesters
Friday 12 November
Te-Won Lee, Mark Girolami, Anthony Bell and Terrence Sejnowski
A Unifying Information-theoretic Framework for Independent Component
Introduced by Stephen Felderhof
Friday 26 November
Dellaportas, Forster, and Ntzoufras On
Bayesian Model and Variable Sepection using MCMC
Introduced by Joe Frankel
Friday 10 December
Andreas Stolcke An
efficient probabilistic context-free parsing algorithm that computes
prefix probabilities, Computational Linguistics 21(2), 165-201.
Introduced by Chris Brew
Friday 21 January
Note: This meeting will be held at 12:30 Faculty Room North, David
The first meeting of this term will involve a talk rather than a paper
discussion (non-PMRG members very welcome):
Sam Roweis from the Gatsby Computational Neuroscience Group at UCL, London
will be talking on
Constrained Hidden Markov Models for Sequence Modeling
By thinking of each state in a hidden Markov model as corresponding to
some spatial region of a fictitious "topology space" it is
possible to naturally define the neighbouring states of any state
as those which are connected in that space.
The transition matrix of the HMM can then be constrained to allow
transitions only between neighbours; this means that all valid state
sequences correspond to connected paths in the topology space.
This strong constraint makes structure discovery in sequences easier.
I show how such *constrained HMMs* can learn to discover underlying
structure in complex sequences of high dimensional data, and apply them
to the problem of recovering mouth movements from acoustic observations
in continuous speech and to learning character sequences in text.
Fiday 3 March
Applying Collins' Models for Categorial Grammars
Julia Hockenmaier will be introducing her work. She has suggested
discussing the paper
"Three Generative, Lexicalised Models for Statistical Parsing", by
which can be found at
She will introduce this paper and say how it relates to her work.
Friday 12 May
I will set the ball rolling this term with
P.J. Green and Duncan J. Murdoch (1998) Exact Sampling for Bayesian
Inference: Towards general purpose algorithms.
From this I hope we will get some overview of exact sampling. For those
who want more detail I have also provided the Propp and Wilson reference
here (quite long).
Friday 8 September
The first meeting of term will involve looking at two papers.
The first is a short tutorial:
Adam Berger "A gentle introduction to iterative scaling"
which will then lead on to wider discussion regarding
S. Della Pietra, V. Della Pietra, and J. Lafferty, Inducing features
of random fields, IEEE Transactions on Pattern Analysis and Machine
Intelligence, 19(4), April 1997, pp. 380-393.
available from http://www.cs.cmu.edu/~lafferty/pubs.html.
See also Lafferty's home
Chris Williams will be introducing this session.
Friday 22 September
We will be discussing
Naftali Tishby, Fernando Pereira, and William Bialek (1999) The
Information Bottleneck Method. Invited paper to the 37th annual
Allerton Conference on Communication, Control, and Computing. 10 pages.
Matthias Seeger will be introducing the paper.
Friday 6 October
We will be discussing
Mike Schuster (2000) Better Generative
Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density
Networks. NIPS1999. This paper is not available in the NIPS online,
and so the copy here is a pdf scanned version, and hence suffers from some
degradation. It is generally readable, but those with access to NIPS
proceedings will probably want to use their "home grown" copies.
Simon King will be introducing this paper.